There has been growing interest and advance in deep energy-based learning. The deep energy-based model specifies an explicit probability density up to a normalization by using a modern bottom-up neural network to parameterize the energy function. The model can be trained by Langevin dynamics-based maximum likelihood estimation. It unifies the bottom-up representation and top-down generation into a single framework, which makes it different from the other generative models, such as generative adversarial net (GAN) and variational auto-encoder (VAE). This tutorial provides a quick introduction of current deep energy-based modeling and learning methodologies. It starts from the background of energy-based models from the perspective of computer vision, and then presents three categories of deep energy-based frameworks, including deep energy-based models in data space, energy-based cooperative learning frameworks, and energy-based models in latent space. This tutorial aims to enable researchers to learn about the current advance of deep energy-based learning and apply the knowledges to other domains.